class_docstr: Class for fitting the Kaplan-Meier estimate for the survival function.
  
  Parameters
  ----------
  alpha: float, optional (default=0.05)
      The alpha value associated with the confidence intervals.
  label: string, optional
      Provide a new label for the estimate - useful if looking at many groups.
  
  Examples
  --------
  .. code:: python
  
      from lifelines import KaplanMeierFitter
      from lifelines.datasets import load_waltons
      waltons = load_waltons()
  
      kmf = KaplanMeierFitter(label="waltons_data")
      kmf.fit(waltons['T'], waltons['E'])
      kmf.plot()
  
  
  Attributes
  ----------
  survival_function_ : DataFrame
      The estimated survival function (with custom timeline if provided)
  median_survival_time_ : float
      The estimated median time to event. np.inf if doesn't exist.
  confidence_interval_ : DataFrame
      The lower and upper confidence intervals for the survival function. An alias of
      ``confidence_interval_survival_function_``. Uses Greenwood's Exponential formula ("log-log" in R).
  confidence_interval_survival_function_ : DataFrame
      The lower and upper confidence intervals for the survival function. An alias of
      ``confidence_interval_``. Uses Greenwood's Exponential formula ("log-log" in R).
  cumulative_density_ : DataFrame
      The estimated cumulative density function (with custom timeline if provided)
  confidence_interval_cumulative_density_ : DataFrame
      The lower and upper confidence intervals for the cumulative density.
  durations: array
      The durations provided
  event_observed: array
      The event_observed variable provided
  timeline: array
      The time line to use for plotting and indexing
  entry: array or None
      The entry array provided, or None
  event_table: DataFrame
      A summary of the life table
code: def plot(self, **kwargs):
      warnings.warn(
          "The `plot` function is deprecated, and will be removed in future versions. Use `plot_survival_function`",
          DeprecationWarning,
      )
      return self.plot_survival_function(**kwargs)
  
imports: from __future__ import annotations
  import warnings
  import numpy
  import pandas
  import typing
  from lifelines.fitters import NonParametricUnivariateFitter
  from lifelines.exceptions import StatError, StatisticalWarning
  from lifelines.utils import _preprocess_inputs, _additive_estimate, _to_1d_array, inv_normal_cdf, median_survival_times, qth_survival_time, check_nans_or_infs, coalesce, CensoringType, pass_for_numeric_dtypes_or_raise_array, check_nans_or_infs
  from lifelines.plotting import loglogs_plot, _plot_estimate
  from lifelines.fitters.npmle import npmle, reconstruct_survival_function, npmle_compute_confidence_intervals
label_desc: Use image node_id 10 for calling the KaplanMeierFitter obj's underlying member method code with example usage: obj.plot() and returns: self